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Dimension of the boomed
Dimension of the boomed






dimension of the boomed dimension of the boomed

Until recently, there are no clear factors and models to determine price moving direction and altitude from high-frequency trading data especially in Chinese mainland stock exchange market.

dimension of the boomed

The field of market microstructure, in which reasons could cause price moving in continuous trading period, is a research mainstream, because many factors affect price variation like big market orders, clouded market orders on the same side, breaking news, market liquidity, and number of participant members. The market microstructure from China will contribute very different order flows from US market composed of limit orders, market orders, and cancellation orders, which are usually discussed in high-frequency trading. In US stock market, Hasbrouck and Saar investigate trading of 100 NASDAQ-listed equity securities on INET, an electronic limit order book, and find that over 35% of limit orders are cancelled within two seconds of submission compared with only 5%, which we computed from most liquidity stocks in Shenzhen stock exchange in Chinese stock market.

dimension of the boomed

IntroductionĮxcessive order cancelations are scrutinized by regulators who view such excess as a possible indicator of manipulative quoting activity by potential stock market manipulators. And the correlations of OEI are very high that may be exploited to predict the price move in the next time window for doing high-frequency trading. In particular, when market’s liquidity is booming, our model’s explanatory power and R-squared increased sharply. (2014) based on market microstructure of Chinese mainland stock market. From time dimension viewpoint, we find the difference of efficiency of limit orders executed, respectively, in bid/ask limit order book, order execution imbalance (OEI), could improve the classic model of Cont et al. In Chinese stock market, the cancellation ratio is very low, and imbalanced order flow prevails most of the time in the trading periods. We propose a new linear model to explain the price move by Level-2 high-frequency data in Chinese mainland stock market.








Dimension of the boomed